Relevant bibliographies by topics / SPSS series in data analysis

Academic literature on the topic 'SPSS series in data analysis'

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Published: 26 July 2024

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Journal articles on the topic "SPSS series in data analysis"

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Jatnika, Ratna, Mustofa Haffas, and Hendriati Agustiani. "Developing the UNPAD SAS (Universitas Padjajaran Statistical Analysis Series) Software." SISFORMA 5, no. 1 (May 31, 2018): 8. http://dx.doi.org/10.24167/sisforma.v5i1.1172.

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Student in Faculty of Psycho­logy think that Statistics is very difficult for them, because Statistics is viewed as a hard science than Psychology which is viewed as a soft science. Various attempts have been made to improve student atti­tudes toward statistics, so that student ha­ve more positive attitudes. One of the ef­forts is to transform the curiculum of Sta­tistics in Faculty of Psychology UniversitasPadjadjaran by adding SPSS (Statistical Packages for Social Sciences) practicum courses since 2009. There are a variety of data analysis con­tained in SPSS can be used for data pro­cessing. However, there are still some sta­tistical data analysis used by students of the Faculty of Psychology that is not ava­ilable in SPSS. The aimed of this research is to develop software namely UniversitasPadjadjaran Statistical Analysis Series, which is statistical data analysis software that consist analysis that does not exist in SPSS or other data analysis software. In this preliminary research, modules are de­veloped only for Database Management and Descriptive Statistics.The software development will be carried out by (SDLC = Software Development Li­fe Cycle). SDLC is a series of step or phase that presents a model for development and lifecycle management software or applica­tions.The resulting software is tested on 144 stu­dents in Psychology Faculty in UniversitasPadjadjaran. The trial results showed that the software is most appropriate and "user friendly" software.
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Sandi A, Arif Setia, Deny Nugroho Triwibowo, Raden Bagus Bambang Sumantri, and Rito Cipta Sigitta Haryono. "PELATIHAN PENGGUNAAN APLIKASI SPSS UNTUK MENUNJANG TUGAS AKHIR MAHASISWA." Jurnal Pengabdian Pada Masyarakat METHABDI 3, no. 2 (December 31, 2023): 195–99. http://dx.doi.org/10.46880/methabdi.vol3no2.pp195-199.

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The training activity on the usage of the Statistical Analysis SPSS Application (Statistical Package for the Social Sciences) was conducted to enhance students' skills in analyzing data to support the completion of their final assignments. This article elucidates the methods and benefits of this training, tailored to suit the students' material needs, aiding them in finalizing their assignments. These sessions were conducted through a series of workshops accommodating students from diverse scientific backgrounds. The outcomes of these activities provided insights into the competency and proficiency regarding SPSS application use in enhancing students' statistical analysis skills. The implications of these activities could serve as a guide for higher education institutions in designing training programs that support the development of student’s academic skills in data analysis, thereby bolstering their final assignment requirements. The results of these activities demonstrate a significant improvement in students' understanding of statistical concepts, SPSS utilization, and the necessary data analysis skills required for final assignment research.
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Zaenudin, Zaenudin, Arie Kurniawan, and M. A. S. Sridjoko Darodjatun. "Trade Analysis of Factors Affecting Sales of Retail Sukuk Series SR009." SKETSA BISNIS 10, no. 1 (July 26, 2023): 65–84. http://dx.doi.org/10.35891/jsb.v10i1.3941.

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English Version Retail Sukuk is one of the mechanisms carried out by the government in fulfilling APBN financing as well as an attractive investment instrument for the public. As an investment instrument, there is a need for research on the determinants of trade in the market. This study uses a quantitative method with the dependent variable trading volume SR09, while the independent variables are retail sukuk prices, deposit rates, sharia deposit profit sharing ratios, retail state bond prices, BI middle rate, inflation, BI 7-Day Repo Rate. . The data used are time-series secondary data for each variable from April 2017 to March 2020. After the data was processed using the SPSS 25 application, it was found that after the data was processed using the SPSS 25 application, the results were that Retail Sukuk Price SR009, Bank Deposit Interest Rate, BI Middle Rate, Negative Effect of Retail Sukuk Trading Volume SR09, Inflation (X6), and BI 7-Day Repo Rate (X7) on Ri Sukuk Trading Volume tel SR09. Meanwhile, the Sharia Banking Profit Sharing Ratio has a positive effect on the Trading Volume of Retail Sukuk SR09. The next result is that the seven variables simultaneously affect the dependent variable. Versi Indonesia Sukuk Ritel merupakan salah satu mekanisme yang dilakukan pemerintah dalam pemenuhan pembiayaan APBN sekaligus sebagai instrumen investasi yang menarik bagi masyarakat. Sebagai instrumen investasi, perlu adanya penelitian tentang faktor-faktor penentu perdagangan di pasar. Penelitian ini menggunakan metode kuantitatif dengan variabel dependen volume perdagangan SR09, sedangkan variabel independennya adalah harga sukuk ritel, suku bunga deposito, nisbah bagi hasil deposito syariah, harga obligasi negara ritel, kurs tengah BI, inflasi, BI 7-Day Repo Rate. . Data yang digunakan adalah data sekunder time-series masing-masing variabel dari bulan April 2017 sampai dengan Maret 2020. Setelah data diolah menggunakan aplikasi SPSS 25, didapatkan hasil bahwa Setelah data diolah menggunakan aplikasi SPSS 25 didapatkan hasil bahwa Harga Sukuk Ritel SR009, Suku Bunga Deposito Bank, Kurs Tengah BI, Pengaruh Negatif Volume Perdagangan Sukuk Ritel SR09, Inflasi (X6), dan BI 7-Day Repo Rate (X7) terhadap Volume Perdagangan Sukuk Ritel SR09. Sedangkan Rasio Bagi Hasil Perbankan Syariah berpengaruh positif terhadap Volume Perdagangan Sukuk Ritel SR09. Hasil selanjutnya adalah ketujuh variabel tersebut secara simultan berpengaruh terhadap variabel dependen.
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Kumar, Varun, Abhay Singh, Mrinmoy Adhikary, Shailaja Daral, Anita Khokhar, and Saudan Singh. "Seasonality of Tuberculosis in Delhi, India: A Time Series Analysis." Tuberculosis Research and Treatment 2014 (2014): 1–5. http://dx.doi.org/10.1155/2014/514093.

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Background. It is highly cost effective to detect a seasonal trend in tuberculosis in order to optimize disease control and intervention. Although seasonal variation of tuberculosis has been reported from different parts of the world, no definite and consistent pattern has been observed. Therefore, the study was designed to find the seasonal variation of tuberculosis in Delhi, India.Methods. Retrospective record based study was undertaken in a Directly Observed Treatment Short course (DOTS) centre located in the south district of Delhi. Six-year data from January 2007 to December 2012 was analyzed. Expert modeler of SPSS ver. 21 software was used to fit the best suitable model for the time series data.Results. Autocorrelation function (ACF) and partial autocorrelation function (PACF) at lag 12 show significant peak suggesting seasonal component of the TB series. Seasonal adjusted factor (SAF) showed peak seasonal variation from March to May. Univariate model by expert modeler in the SPSS showed that Winter’s multiplicative model could best predict the time series data with 69.8% variability. The forecast shows declining trend with seasonality.Conclusion. A seasonal pattern and declining trend with variable amplitudes of fluctuation were observed in the incidence of tuberculosis.
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Jiang, Fengchang, John Awaitey, and Haiyan Xie. "Analysis of Construction Cost and Investment Planning Using Time Series Data." Sustainability 14, no. 3 (February 1, 2022): 1703. http://dx.doi.org/10.3390/su14031703.

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Construction costs and investment planning are the decisions made by construction managers and financial managers. Investment in construction materials, labor, and other miscellaneous should consider their huge costs. For these reasons, this research focused on analyzing construction costs from the point of adopting multivariate cost prediction models in predicting construction cost index (CCI) and other independent variables from September 2021 to December 2022. The United States was selected as the focal country for the study because of its size and influence. Specifically, we used the Statistical Package for Social Sciences (SPSS) software and R-programming applications to forecast the elected variables based on the literature review. These forecasted values were compared to the CCI using Pearson correlations to assess influencing factors. The results indicated that the ARIMA model is the best forecasting model since it has the highest model-fit correlation. Additionally, the number of building permits issued, the consumer price index, the amount of money supply in the country, the producer price index, and the import price index are the influencing factors of investments decisions in short to medium ranges. This result provides insights to managers and cost planners in determining the best model to adopt. The improved accuracies of the influencing factors will help to enhance the control, competitiveness, and capability of futuristic decision-making of the cost of materials and labor in the construction industry.
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Liu, Wuwei, and Jingdong Yan. "Financial Time Series Image Algorithm Based on Wavelet Analysis and Data Fusion." Journal of Sensors 2021 (April 26, 2021): 1–11. http://dx.doi.org/10.1155/2021/5577852.

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In recent years, people are more and more interested in time series modeling and its application in prediction. This paper mainly discusses a financial time series image algorithm based on wavelet analysis and data fusion. In this research, we conducted an in-depth study on the scale decomposition sequence and wavelet transform sequence in different scale domains of wavelet transform according to the scale change rule based on wavelet transform. We use wavelet neural network with different input neurons and hidden neurons to predict, respectively. Finally, the prediction results are integrated into the final prediction results based on the original time series by using wavelet reconstruction technology. Using RBF algorithm in neural network and SPSS Clementine, the wavelet transform sequences on five scales are modeled. Each network model has three layers: one input layer, one hidden layer, and one output layer, and each output layer has only one output element. In order to compare the prediction effect of the model proposed in this study, the ordinary RBF network is used to model and predict the log yield itself. When the input sample is 5, the minimum mean square error is obtained when the hidden layer is 6, and the mean square error is 1.6349. The mean square error of the training phase is 0.0209, and the validation error is 1.6141. The results show that the prediction results of the wavelet prediction method combined with the RBF network prediction method are better than those of wavelet prediction or RBF network prediction.
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He, Juan, and Xin Ma. "Empirical Analysis on Regression Model Based on Time Series and Cross Section Data of Hot-Rolled Coil Price." Applied Mechanics and Materials 198-199 (September 2012): 876–79. http://dx.doi.org/10.4028/www.scientific.net/amm.198-199.876.

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This paper, considering the lack of empirical analysis on price risk of inventory based on large-sample, introduces factor analysis method, multiple regression method and SPSS 17.0 to make an empirical analysis on hot-rolled coil price forecasting model by analyzing the characteristics of its price fluctuation. The results show that there is small difference between forecast result and the actual one and that this forecast method is feasible.
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Ghani, Usman, Peter Toth, and Fekete David. "Predictive Choropleth Maps Using ARIMA Time Series Forecasting for Crime Rates in Visegrád Group Countries." Sustainability 15, no. 10 (May 16, 2023): 8088. http://dx.doi.org/10.3390/su15108088.

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Geographical mapping has revolutionized data analysis with the help of analytical tools in the fields of social and economic studies, whereby representing statistical research variables of interest as geographic characteristics presents visual insights. This study employed the QGIS mapping tool to create predicted choropleth maps of Visegrád Group countries based on crime rate. The forecast of the crime rate was generated by time series analysis using the ARIMA (autoregressive integrated moving averages) model in SPSS. The literature suggests that many variables influence crime rates, including unemployment. There is always a need for the integration of widespread data insights into unified analyses and/or platforms. For that reason, we have taken the unemployment rate as a predictor series to predict the future rates of crime in a comparative setting. This study can be extended to several other predictors, broadening the scope of the findings. Predictive data-based choropleth maps contribute to informed decision making and proactive resource allocation in public safety and security administration, including police patrol operations. This study addresses how effectively we can utilize raw crime rate statistics in time series forecasting. Moreover, a visual assessment of safety and security situations using ARIMA models in SPSS based on predictor time-series data was performed, resulting in predictive crime mapping.
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Zheng, Jie, Shunping Ouyang, Jian Kang, and Youcun Xiao. "Prediction Method Based on Time Series." Highlights in Science, Engineering and Technology 47 (May 11, 2023): 24–31. http://dx.doi.org/10.54097/hset.v47i.8160.

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In order to predict the development trend of a set of time series data in Yunnan Province in 2023, we collected the daily number of confirmed cases in Yunnan Province from 2020 to 2022. Firstly, according to the data analysis of the current situation of prevention and control in Yunnan Province, ARIMA model is used to predict in SPSS system. The final results showed that the number of confirmed cases in Yunnan Province in each month of 2023 showed a slow upward trend, eventually reaching more than 400. It is recommended to pay attention to the publicity of prevention and control in each state and the popularization of medical equipment.
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Zsofia J., TOSZEGI, ERAT David, and VARGA Aranka. "NEURODIVERSITY IN HIGHER EDUCATION: TIME SERIES DATA ANALYSIS OF THE NEPTUN UNIFIED EDUCATION SYSTEM." Academis notes. Series: Pedagogical sciences 5 (November 23, 2023): 82–94. http://dx.doi.org/10.59694/ped_sciences.2023.05.0082.

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This paper is linked to the current Diversity, Equality, and Inclusion research in higher education. It uses the Process Model of Inclusion to describe educational data of student groups with special needs, with a particular focus on neurodiversity at the University of Pécs (UP). It explores differences that can be identified when comparing student groups over a 10-year time-series analysis of student data from the Neptun Unified Education System (N = 47,194). Using SPSS, we explore admission rates and faculty distribution at entry, supporting factors during the process, and achievement indicators at the time of exiting university. Our results reveal that neurodivergent students appear in the largest proportion among student groups, nearly half of them receive scholarships, about a third of them acquire language proficiency exam certificates before graduation, and only a quarter of them receive dormitory placement. However, the logistic regression analysis substantiates the rather surprising outcome that neurodivergent students are the most likely to graduate successfully from UP, and they are the least likely to defer semesters during their course of study. Our macro-statistical data provide valuable starting points for our ongoing qualitative research that strives to look beyond these numbers for explanations. Keywords: Process Model of Inclusion, higher education, neurodiversity.
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Dissertations / Theses on the topic "SPSS series in data analysis"

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Brunsdon, T. M. "Time series analysis of compositional data." Thesis, University of Southampton, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.378257.

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Guthrey, Delparde Raleigh. "Time series analysis of ozone data." CSUSB ScholarWorks, 1998. https://scholarworks.lib.csusb.edu/etd-project/1788.

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Clarke, Liam. "Nonlinear time series analysis of data streams." Thesis, University of Oxford, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.401147.

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Jiang, Chunyu. "DATA MINING AND ANALYSIS ON MULTIPLE TIME SERIES OBJECT DATA." Wright State University / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=wright1177959264.

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Mazel, David S. "Fractal modeling of time-series data." Diss., Georgia Institute of Technology, 1991. http://hdl.handle.net/1853/13916.

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Rawizza, Mark Alan. "Time-series analysis of multivariate manufacturing data sets." Thesis, Massachusetts Institute of Technology, 1996. http://hdl.handle.net/1721.1/10895.

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Rodrigues, David Francis. "Modelling election poll data using time series analysis." Thesis, University of Kent, 2009. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.527582.

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Leung, Caleb Chee Shan. "Time series modelling of birth data." Thesis, Canberra, ACT : The Australian National University, 1995. http://hdl.handle.net/1885/118134.

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Three basic methods namely cohort component projection methods, statistical time series methods and structural modelling methods are discussed for the purpose of forecasting births, with the main focus on univariate time series methods. A general autoregressive integrated moving average model for birth time series is developed from the mathematical demographic renewal equation for births. The four-stage Box-Jenkins modelling method of model identification, estimation, diagnosis and forecasting is investigated in detail. This method is employed to model and forecast Australian birth time series. Finally, the comparison between time series forecasts and cohort component projections of births for Australia is made.
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Wan, Xiaogeng. "Time series causality analysis and EEG data analysis on music improvisation." Thesis, Imperial College London, 2014. http://hdl.handle.net/10044/1/23956.

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This thesis describes a PhD project on time series causality analysis and applications. The project is motivated by two EEG measurements of music improvisation experiments, where we aim to use causality measures to construct neural networks to identify the neural differences between improvisation and non-improvisation. The research is based on mathematical backgrounds of time series analysis, information theory and network theory. We first studied a series of popular causality measures, namely, the Granger causality, partial directed coherence (PDC) and directed transfer function (DTF), transfer entropy (TE), conditional mutual information from mixed embedding (MIME) and partial MIME (PMIME), from which we proposed our new measures: the direct transfer entropy (DTE) and the wavelet-based extensions of MIME and PMIME. The new measures improved the properties and applications of their father measures, which were verified by simulations and examples. By comparing the measures we studied, MIME was found to be the most useful causality measure for our EEG analysis. Thus, we used MIME to construct both the intra-brain and cross-brain neural networks for musicians and listeners during the music performances. Neural differences were identified in terms of direction and distribution of neural information flows and activity of the large brain regions. Furthermore, we applied MIME on other EEG and financial data applications, where reasonable causality results were obtained.
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Perez, Melo Sergio. "Statistical Analysis of Meteorological Data." FIU Digital Commons, 2014. http://digitalcommons.fiu.edu/etd/1527.

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Some of the more significant effects of global warming are manifested in the rise of temperatures and the increased intensity of hurricanes. This study analyzed data on Annual, January and July temperatures in Miami in the period spanning from 1949 to 2011; as well as data on central pressure and radii of maximum winds of hurricanes from 1944 to present. Annual Average, Maximum and Minimum Temperatures were found to be increasing with time. Also July Average, Maximum and Minimum Temperatures were found to be increasing with time. On the other hand, no significant trend could be detected for January Average, Maximum and Minimum Temperatures. No significant trend was detected in the central pressures and radii of maximum winds of hurricanes, while the radii of maximum winds for the largest hurricane of the year showed an increasing trend.
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Books on the topic "SPSS series in data analysis"

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Sweet, Stephen A. Data analysis with SPSS. Boston: Allyn & Bacon, 1999.

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Assessment, Programme for International Student. PISA data analysis manual: SPSS. 2nd ed. Paris: OECD, 2009.

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Norušis, Marija J. The SPSS guide to data analysis. Chicago: SPSS, 1986.

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Norušis, M. J., and M. J. Norušis. SPSS 10.0 guide to data analysis. Upper Saddle River, N.J: Prentice Hall, 2000.

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Inc, SPSS, ed. SPSS 7.5 guide to data analysis. Upper Saddle River, N.J: Prentice Hall, 1997.

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Inc, SPSS, ed. SPSS 8.0 guide to data analysis. Upper Saddle River, N.J: Prentice Hall, 1998.

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Organisation for Economic Co-operation and Development. PISA 2003 Data Analysis Manual: SPSS. Paris: OECD Publishing, 2005.

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Norušis, M. J. SPSS 9.0 guide to data analysis. Upper Saddle River, N.J: Prentice Hall, 1999.

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Carver, Robert H. Doing data analysis with SPSS version14. Belmont, CA: Thomson-Brooks/Cole, 2006.

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Norušis, M. J. The SPSS guide to data analysis. Chicago, IL: SPSS, 1986.

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Book chapters on the topic "SPSS series in data analysis"

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Okello, Gabriel Otieno. "Analysis of Time Series Data." In Simplified Business Statistics Using SPSS, 377–404. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003292654-20.

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De Cantis, Stefano, and Anna Maria Taormina. "The Assessment of Second Primary Cancers (SPCs) in a Series of Splenic Marginal Zone Lymphoma (SMZL) Patients." In From Data and Information Analysis to Knowledge Engineering, 708–15. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/3-540-31314-1_87.

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Wendler, Tilo, and Sören Gröttrup. "Factor Analysis." In Data Mining with SPSS Modeler, 513–85. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28709-6_6.

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Wendler, Tilo, and Sören Gröttrup. "Cluster Analysis." In Data Mining with SPSS Modeler, 587–712. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28709-6_7.

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Wendler, Tilo, and Sören Gröttrup. "Cluster Analysis." In Data Mining with SPSS Modeler, 623–752. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-54338-9_7.

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Wendler, Tilo, and Sören Gröttrup. "Factor Analysis." In Data Mining with SPSS Modeler, 547–622. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-54338-9_6.

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Mat Roni, Saiyidi, and Hadrian Geri Djajadikerta. "SPSS Basics." In Data Analysis with SPSS for Survey-based Research, 9–14. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0193-4_2.

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Reddy, M. Venkataswamy. "Running Data Analysis Using Spss." In Statistical Methods in Psychiatry Research and SPSS, 323–66. Second edition. | Toronto ; New Jersey : Apple Academic Press, 2018.: Apple Academic Press, 2019. http://dx.doi.org/10.1201/9780429023309-22.

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Marques de Sá, Joaquim P. "Data Structure Analysis." In Applied Statistics Using SPSS, STATISTICA and MATLAB, 283–304. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-662-05804-6_8.

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Reddy, M. Venkataswamy. "Survival Analysis and Time Series Analysis." In Statistical Methods in Psychiatry Research and SPSS, 187–96. Second edition. | Toronto ; New Jersey : Apple Academic Press, 2018.: Apple Academic Press, 2019. http://dx.doi.org/10.1201/9780429023309-14.

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Conference papers on the topic "SPSS series in data analysis"

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Han, Xinrong. "An Automated Time Series Modeling and Forecasting Approach based on SPSS Statistics." In ICCDA 2021: 2021 The 5th International Conference on Compute and Data Analysis. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3456529.3456541.

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Sokoya, A. A. "Comparative Study of Institutional Facilities as Predictors of Students Choice of Schools: Case Study of Public Tertiary Institutions in Lagos, Nigeria." In Advances in Multidisciplinary and Scientific Research Publication Series. Society for Multidisciplinary and Advanced Research Techniques - Creative Research Publishers, 2021. http://dx.doi.org/10.22624/aims/isteams-2021/v28p3.

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Institutional facilities often determine students’ choice of enrolment into tertiary institution and level of academic performance Rudhumbu,Tirumali and Kumari, (2017). Thus institutions invest more on installation and maintenance of facilities like business enterprise to compete favorably with their counterparts in order to have good number of students and to improve institutional image and status (Gibbs, 2001). Despite institutions stringent requirements of students’ academic results from high school, parents are more concerned of institutional image; safety and campus security. This paper focused on institutional facilities as predictors of students’ choice of enrolment into tertiary institutions for further studies. The study population comprised of year two students in all fields of study from each level of tertiary institutions (college of education, polytechnic and university) in Lagos, Nigeria. It also examined the impact of available facilities ranging from library facilities and services, internet facilities, campus accommodation and proximity to the library and lecture rooms, lecturers experience and academic qualifications are inclusive facilities examined in this study. The study also mirror on the admission requirements of the various schools under study, the structural facilities need of all categories of students in the study; the place of the physically challenged students inclusive. The study adopted survey designed research with the aid of both self structured questionnaire, observation and interview as research instruments for clarification. The services of research assistance hinged on the distribution and collation of questionnaires for the study. Statistical Package for the Social Sciences (SPSS) software was used to analyse the data. Conclusion and recommendations was drawn based on the research findings. Keywords: Institutional facilities, library, students’ enrolment.
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T. N, Nithin. "Statistical Analysis of Jobsite Safety Assessment in Construction." In The International Conference on scientific innovations in Science, Technology, and Management. International Journal of Advanced Trends in Engineering and Management, 2023. http://dx.doi.org/10.59544/iywx3450/ngcesi23p85.

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Construction industry is one of the most hazardous industries. Accidents may occur during construction which results in serious injuries and death to employees and also may cause severe damage to equipment. Hence it is important to prevent or minimize accidents in the construction site. Safety during construction is very important for each employee. This project deals with job site safety analysis in construction site by identifying the causes for accidents and implementing solutions for preventing the accident in the construction industry. From the literatures it was studied that statistical analysis is better way to analyse the construction jobsite safety and important factors that are affecting the safety in construction is identified from the literatures. Questionnaire regarding the factors affecting safety in construction site is also prepared. Then primary data should be collected from the various construction companies and secondary data should be collected from their clients. Then the data’s are ranked by two different techniques, primary data was ranked by using Relative important index method and secondary data was ranked by the Garrett’s ranking technique. Then the companies which are following safe safety procedures should be found out using Statistical analysis. This data is analysed in Statistical Package for Social Science (SPSS) software and the major factors which affect safety in construction should found out through comparison of primary and secondary data by paired sample t-test. With this approach, it is possible to evaluate the uncertainty regarding safety in construction.
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Doucek, Petr, and Lea Nedomova. "Completion Rate on Informatic Studies- Prague University of Economics and Business." In Society’s Challenges for Organizational Opportunities: Conference Proceedings. University of Maribor Press, 2022. http://dx.doi.org/10.18690/um.fov.3.2022.14.

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In 2019, the Czech society overcame a regular decrease in the number of university applicants, where the number of teenage potential applicants reached its minimum and began to go up again the following year. However, even this fact did not ease the pressure on rational education spendings. This naturally leads to an increasing need to monitor the success rate of studies. In this paper, we analyze the data of time series starting in 2010, which contain information about students of full-time and distance learning study programs at the Faculty of Informatics and Statistics of Prague University of Economics and Business. We evaluated the data, using the standard functions of MS Excel, SPSS software package and R product. The results of our analysis show that the total “Completion Rate” in informatics study programs ranges between 75.50% and 40.29%. The average length of successful studies is between 5.69 and 4.58 semesters in study programs for which students pay tuition fees. The median of the duration of successful studies is between 4 and 6 semesters. On the other hand, dropouts study between 2.673 and 3.90 semesters. The median is between 2 and 3 semesters.
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Moura, Jackson Roberto de, Jackson Roberto de Moura Júnior, Jackline Zonta de Moura, Julia Zonta de Moura, and Nathalia de Melo Carmanini. "CO2 LASER THERAPY IMPROVING THE SEX LIFE OF WOMEN AFTER BREAST CANCER TREATMENT: 92 CASES." In XXIV Congresso Brasileiro de Mastologia. Mastology, 2022. http://dx.doi.org/10.29289/259453942022v32s1049.

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Objective: The aim of this study was to verify the response to CO2 laser therapy regarding vaginal dryness in women submitted to this treatment in a facility in the state of Minas Gerais, Brazil. Methods: This is a prospective descriptive case series based on patients treated by the same team with the same Monalisa device of the Beker company. The treatment consisted of three sessions, adopting the same energy and pulse pattern from January 2019 to December 2021, with records of predefined information and the use of the R and SPSS PC software for data analysis. Results: A total of 92 women with a mean age of 60.3 years +10.6 (ranging from 28 to 92 years) were treated in the facility for vaginal dryness. Vaginal dryness improved in 94.6% of cases. Sex life improved in 81% of cases, and sexual intercourse increased by 66%. They would all recommend the examination to a friend. We found no statistical significance for the use of tamoxifen (12 cases), anastrozole (8 cases), previous chemotherapy (27 cases), and time between surgery and laser therapy (p>0.05). Conclusion: The study shows good results, with CO2 laser therapy improving vaginal dryness and sex life; however, the sample limitation prevents the statistical analysis of subgroups.
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Ola, George Tayo, Christiana Adetunde, and Oluwamumibori Victoria Agboola. "EMPATHY TRAINING AS A MEANS OF PREVENTING BULLYING IN SELECTED SECONDARY SCHOOLS: ALIMOSHO LOCAL GOVERNMENT AREA, LAGOS, NIGERIA." In SOCIOINT 2022- 9th International Conference on Education and Education of Social Sciences. International Organization Center of Academic Research, 2022. http://dx.doi.org/10.46529/socioint.202237.

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Bullying is a maladaptive behaviour that children and adolescents face daily in schools all around the world. It is prevalent in today's schools and is commonly found in secondary schools. It is a complicated social problem that can have serious negative implications for both bullies and victims, especially when the victim is physically or psychologically harmed. The study, therefore, examines empathy training among students to prevent bullying behaviour in selected secondary schools in Alimosho local government area of Lagos state. A survey method was utilized using the structured questionnaire as an instrument of data collection. Utilizing a simple random sampling, three hundred and seventy-six copies of the questionnaire were administered to students in the four secondary schools. However, three hundred and thirty were retrieved which were used for the analysis. Quantitative data were tested using Quantitative data was analysed utilizing the descriptive statistics with the aid of the Statistical Package for Social Science (SPSS) version 23. A descriptive analysis was used with the use of frequency counts and percentages for the analysis of the data. Findings of the study shows that many victims of bullying get involved in substance abuse and lose interest in schooling. Therefore, empathy training and encouraging victims to make timely reports on incidences of bullying are crucial to mitigating bullying and its consequences in secondary schools in Lags State. Keywords: Bullying, Empathy training, prevention of bullying, Secondary schools
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Kim, Mi-Lyang, Rak-Heung Choi, and Myeong-Ha Lee. "SERIOUS LEISURE LEVEL AND WORK OF KOREAN WOMEN'S BASEBALL CLUBS - LEISURE CONFLICT." In SSHRA 2024 –Social Science & Humanities Research Association International Conference, 18-19 January, Tokyo. Global Research & Development Services, 2024. http://dx.doi.org/10.20319/icssh.2024.7273.

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The purpose of this study is to verify the differences and relationships in work-leisure conflict based on the level of serious leisure among female baseball enthusiasts who participate in baseball as a serious leisure activity. To achieve this goal, a survey was conducted using a purposive sampling method among female baseball enthusiasts. Out of the collected surveys, a total of 175 were used for the final analysis, excluding 34 responses that were deemed insincere. In this study, exploratory factor analysis, cluster analysis, K-means cluster analysis, one-way analysis of variance (ANOVA), and multiple regression analysis methods were employed. The data collected were analyzed using SPSS 25.0 Program and AMOS 26.0 Program. The results of the analysis showed the following: Firstly, the analysis of differences in work-leisure conflict based on the level of serious leisure revealed a significant difference among female baseball enthusiasts. Secondly, the differences in the four sub-factors of work-leisure conflict based on the level of serious leisure were examined. Post-hoc analysis results indicated that only the negative affect_WL and time pressure_WL factors had significant differences based on the level of serious leisure. Thirdly, the impact of sub-factors of serious leisure on work-leisure conflict showed that only the personal effort factor had a negative influence on work-leisure conflict.This study aimed to elucidate the relationship between the level of serious leisure among female baseball enthusiasts participating in baseball as serious leisure and work-leisure conflict. Through this research, a deeper understanding of the lives of women participating in sports activities as serious leisure is sought.
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Paul Páez Almeida, Omar, Ana Alvarez Sanchez, and Alexis Suarez Del Villar Labastida. "Projection of the Future of the Green Savings Account Product in a Financial Institution." In 8th International Conference on Human Interaction and Emerging Technologies. AHFE International, 2022. http://dx.doi.org/10.54941/ahfe1002799.

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The new financial product that enters the Ecuadorian market, this product is called Green Savings Account belonging to a bank. Due to the fact that at present there is no history of projections for the following years due to the short life of this product, it is essential to know what its behavior will be in the future using scientific and statistical methods, which is why the forecast of the green account until 2024 will help in the annual planning of the financial institution, by providing relevant information that allows improving the profitability of the company, not only in the short term but also in the long term. The proposal began with an analysis of short-term forecasts using time series models, and it was found that the method of weighted moving averages using the Microsoft Excel SOLVER tool is ideal for this work, with a mean absolute deviation of 173 counts. . This previous study allowed the real demand data to be statistically modeled and easy to handle to determine long-term projections, this will be done using the Minitab and SPSS software, simulating here different linear and non-linear regression models. Finally, it was found that the exponential regression model is the ideal one to carry out the forecast of the product, since it has the highest level of confidence of all the analyzed models of 73.8%, thus projecting a growth of 470.79% for the month of January of year 2024 comparing with the real demand of the month of January of the year 2021.
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Stoica, Ivona, Andra Dobrescu, Anamariacatalina Radu, and Mihai Orzan. "STUDENT PERCEPTION REGARDING THE DEPLOYMENT OF ONLINE COURSES IN HIGHER EDUCATION INSTITUTIONS." In eLSE 2014. Editura Universitatii Nationale de Aparare "Carol I", 2014. http://dx.doi.org/10.12753/2066-026x-14-133.

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Technology has spectalary progressed over the last years and has brought various benefits to educational environment throughout the implementation of e -learning concept which was quickly adopted by many institutions of higher education. Learning using online environment has been considered a great challenge that brought on the one hand a number of benefits on the other hand a number of disadvantages. So it is that many teachers in various fields have tried to implement this concept and observe student reaction to this way of assimilating information. This paper aims to determine student perception regarding deployment of online courses in higher education institutions. This takes into account a series of factors that have the ability to influence how students perceive this method of teaching and transmitting the information. The main factors taken into account in the analysis we conducted, are: the benefits that can be obtained by attending a course conducted in the online environment , disadvantages perceived in the online user experience and safety information using this communication channel. This paper is based on a quantitative research that was conducted among students to rich out to their perception about the deplyment of courses in the online environment. The quantitative study was conducted on a sample of 142 respondents . The research took place in december 2013, in Bucharest. Information was collected using a questionnaire consisting of 18 questions which was established based on the research objectives. The method used was the online survey method and the instrument was an online questionnaire designed on wesurvey platform. For testing the operational model of student perception regarding the deployment of online courses, data were processed using IBM SPSS Statistics 20
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Gaili, Wang. "Evaluation of Online Teaching based on SPSS Data Analysis." In 2022 International Conference on Computers, Information Processing and Advanced Education (CIPAE). IEEE, 2022. http://dx.doi.org/10.1109/cipae55637.2022.00040.

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Reports on the topic "SPSS series in data analysis"

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Du, Han. Applied Longitudinal Data Analysis in R and SPSS. Instats Inc., 2024. http://dx.doi.org/10.61700/kdliyn5fqb7tr1173.

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Longitudinal data facilitates exploring intra-individual change over time and differences across people in these changes. This workshop equips participants with essential skills to analyze data and report results of longitudinal research effectively, with all worked exercises completed using R and SPSS. The workshop will explore techniques including multilevel modeling, latent growth curve models, cross-lagged models, longitudinal mediation models, longitudinal dyadic models, and other models.
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Beam, Craig A., Emily F. Conant, Harold L. Kundel, Ji-Hyun Lee, Patricia A. Romily, and Edward A. Sickles. Time-Series Analysis of Human Interpretation Data in Mammography. Fort Belvoir, VA: Defense Technical Information Center, January 2005. http://dx.doi.org/10.21236/ada434583.

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Venugopal, Niveditha. Annotation-Enabled Interpretation and Analysis of Time-Series Data. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.6592.

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HYDROLOGIC ENGINEERING CENTER DAVIS CA. Statistical Analysis of Time Series Data (STATS). Users Manual (Preliminary). Fort Belvoir, VA: Defense Technical Information Center, May 1987. http://dx.doi.org/10.21236/ada204568.

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Crowson, Michael. Mediation, Moderation, and Conditional Process Analysis using PROCESS. Instats Inc., 2024. http://dx.doi.org/10.61700/g3k1z8egr15e01173.

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This 3-day workshop provides a comprehensive understanding of mediation, moderation, and conditional process analysis using Hayes’ Process Macro with IBM SPSS, essential for advanced data analysis in academic research. Led by Michael Crowson, participants will gain theoretical knowledge and practical skills to enhance the rigor and depth of their research, with an official Instats certificate of completion provided at the conclusion of the seminar.
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Poyneer, L., and D. Palmer. Time series analysis of Adaptive Optics wave-front sensor telemetry data. Office of Scientific and Technical Information (OSTI), March 2004. http://dx.doi.org/10.2172/15020372.

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Micheli, Leonardo, Matthew T. Muller, Michael G. Deceglie, and Daniel Ruth. Time Series Analysis of Photovoltaic Soiling Station Data: Version 1.0, August 2017. Office of Scientific and Technical Information (OSTI), September 2017. http://dx.doi.org/10.2172/1390775.

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Czaplewski, Raymond L., and Mike T. Thompson. Model-based time-series analysis of FIA panel data absent re-measurements. Ft. Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station, 2013. http://dx.doi.org/10.2737/rmrs-rp-102.

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Attanasio, Orazio, and Margherita Borella. Stochastic Components of Individual Consumption: A Time Series Analysis of Grouped Data. Cambridge, MA: National Bureau of Economic Research, August 2006. http://dx.doi.org/10.3386/w12456.

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Poranki, Kameswari Sai Madhuri. Demand Forecasting of Manufacturing data using Machine Learning and Time series Analysis. Ames (Iowa): Iowa State University, August 2022. http://dx.doi.org/10.31274/cc-20240624-498.

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